1. Field of the Invention
The present invention is generally related to the collection, processing, and presentation of alternative information source content to a user and, in particular, the selective and automated generation of source content alternatives based on content relationships and user behavioral patterns to support the recommendation of alternative content sources.
2. Description of the Related Art
There are an increasing number of typically entertainment oriented media items, such as music, books, videos, and other content sources, available for purchase by users. A currently existing system, available to at least some users, is capable of presenting the details of over 300,000 individual music compact discs alone for purchase by a user. The collection of source content is growing with the continual addition of new content titles as well as the development and adoption of new content technologies, such as MP3, digital music software. Thus, a potential purchaser faces a significant investment of time and expense to comfortably select an appropriate item for purchase.
Existing source content selection systems are quite ineffective in supporting content searches much beyond using artist, collection, and title. Users therefore typically confine their searches to just those media items that are independently known to them or are aware of through other sources of media information. These other sources are typically sufficient to provide indications of whether and which segments of the general population might appreciate particular content items. No indication is given and none can be reliably inferred as to whether a particular user will enjoy or appreciate a given item.
There is, at least for entertainment media content, some acceptance of the belief that a user""s appreciation of particular content items can suggest the user""s likely appreciation of other content titles. Systems built to exploit this belief have met with limited results. One known system, apparently a neural-net based expert system, determines and provides recommendation of other content titles based purely on the similarities between users without considering the relationships between the music items from a content or contextual point of view. These systems have the disadvantage that they require an initial xe2x80x9cteachingxe2x80x9d period where the recommendations given to users are likely to be inaccurate. Another disadvantage is that the user does not understand the reasoning behind the recommendations and therefore does not trust the recommendations. The absence of confidence in whatever recommendations are given directly reduces the utility of the system. Additionally, such systems tend to generate recommendations that reflect the lowest common denominator between broad users tastes. As a result, these systems typically provide recommendations reflecting potential appreciation within a single generic style, such as only 1980""s pop music. These systems do not appear to be effectively capable of providing recommendations across a diverse range of music, such as Death Metal and Classical.
Another known system recommends particular content items based on the given content or style of the item. Such systems are generally established by hand, requiring a broad, yet detailed, understanding of each media item. Establishing even basic knowledge-based systems requires a substantial investment in time and other costs. Therefore, these systems typically employ simplistic relationships between items, such as broad categories, such as Drama and Comedy, for relating content. Since these categories contain large numbers of content items, any user selection against the categories is likely to return an also large set of recommendations and, therefore, is unlikely to be significantly useful to a user.
Finally, both of these existing systems produce recommendations that are effectively final end-points in the recommendation search. No clear ability is provided for users to explore further items related to the recommendations. Thus, the user is often left with recommendations, which are almost correct, but which don""t raise the user""s propensity to consume to the level required to purchase/consume the content.
Therefore, a general purpose of the present invention to provide a system that combines content-based filtering and progressively refined collaborative-based filtering to deliver a set of media item recommendations that are consistent with a user""s personal media content interests.
This purpose is achieved in the present invention by providing a system and method of providing media content recommendations through a computer server system connected to a network communications system. The computer server system preferably has access to a first database of media content items including media content and related information and a media content filter identifying and providing qualifying attribute relationship data for media content items within the first database. The media content recommendations are particularly tailored to the personalized interests of a user through sequence of steps including presenting media content items through a network-connected interface to the user for review and consideration of potential personal interest, monitoring the consideration of the media content items implied through the user directed navigation among the presented media content items and user requests for related information; collecting the monitored data to develop a user weighted data set reflective of the user""s relative consideration of the media content items; and evaluating the user weighted data set in combination with the media content filter to identify a set of media content items accessible from the first database for re-presentation to the user.
Thus, the operation of the present system reflects the consideration that media content items, such as music, video, and other forms of content, can be interrelated based on multiple characterizing attributes. The strength of these characterizing attributes, or similarities, is used to further define these content-based relationships, even as between quite different forms or types of media content. An additional aspect of the operation of the present invention allows for the progressive or continuing collaborative, including self-collaborative, development of such content-based relationships.
An advantage of the present invention, therefore, is that the provided combination of content and collaborative recommendation systems enables the delivery of recommendations that are particularly tailored to the personalized interests of a user.
Another advantage of the present invention is that the system flexibly determines a scope of applicable similarities between a particular and other users and recommends items within the applicable scope.
A further advantage of the present invention is that the self-collaborative relationships developed for individual users of the system permit the development of individualized recommendations even where the group collaborative relationships reflect the choices of users with highly diverse media content interests.
Still another advantage of the present invention is that the system enables multi-level media content relationship information to be captured and used as data evaluateable in providing particularized media content item recommendations.
Yet another advantage of the present invention is that implicit and explicit collaborative data is captured from and in consideration of particular users, supporting both the continuing development of both group and personal interest profiles. The implicit collaborative data is advantageously obtained from a user""s self-directed actions of reviewing and considering different media content items. Thus, the selection of items to review and the length and nature of the consideration of such items inferentially reflects the user""s relative interest in particular media content items. Confidence levels in the inferences drawn can also be developed and refined through the continued monitoring of user actions in reviewing and considering the same and closely similar media content items. The explicit information provided by users regarding the level and nature of their interest in different media content items provides high-confidence information that can be incorporated into the group and individualized collaborative data.